Recent advances in anomaly detection in Internet of Things: Status, challenges, and perspectives
This paper provides a comprehensive survey of anomaly detection for the Internet of Things
(IoT). Anomaly detection poses numerous challenges in IoT, with broad applications …
(IoT). Anomaly detection poses numerous challenges in IoT, with broad applications …
UniTS: A unified multi-task time series model
Although pre-trained transformers and reprogrammed text-based LLMs have shown strong
performance on time series tasks, the best-performing architectures vary widely across …
performance on time series tasks, the best-performing architectures vary widely across …
Epilepsy detection in 121 patient populations using hypercube pattern from EEG signals
Background Epilepsy is one of the most commonly seen neurologic disorders worldwide
and has generally caused seizures. Electroencephalography (EEG) is widely used in …
and has generally caused seizures. Electroencephalography (EEG) is widely used in …
[HTML][HTML] Online model-based anomaly detection in multivariate time series: Taxonomy, survey, research challenges and future directions
Time-series anomaly detection plays an important role in engineering processes, like
development, manufacturing and other operations involving dynamic systems. These …
development, manufacturing and other operations involving dynamic systems. These …
Unsupervised maritime anomaly detection for intelligent situational awareness using AIS data
With the mandatory implementation of the automatic identification system and the rapid
advancement of relevant satellite communication technologies, a vast amount of vessel …
advancement of relevant satellite communication technologies, a vast amount of vessel …
Federated graph anomaly detection via contrastive self-supervised learning
Attribute graph anomaly detection aims to identify nodes that significantly deviate from the
majority of normal nodes, and has received increasing attention due to the ubiquity and …
majority of normal nodes, and has received increasing attention due to the ubiquity and …
[HTML][HTML] Temporal Logical Attention Network for Log-Based Anomaly Detection in Distributed Systems
Detecting anomalies in distributed systems through log analysis remains challenging due to
the complex temporal dependencies between log events, the diverse manifestation of …
the complex temporal dependencies between log events, the diverse manifestation of …
[HTML][HTML] Anomaly-based error and intrusion detection in tabular data: No DNN outperforms tree-based classifiers
Recent years have seen a growing involvement of researchers and practitioners in crafting
Deep Neural Networks (DNNs) that seem to outperform existing machine learning …
Deep Neural Networks (DNNs) that seem to outperform existing machine learning …
From anomaly detection to classification with graph attention and transformer for multivariate time series
C Wang, G Liu - Advanced Engineering Informatics, 2024 - Elsevier
Numerous industrial environments and IoT systems in the real world contain a range of
sensor devices. These devices, when in operation, produce a large amount of multivariate …
sensor devices. These devices, when in operation, produce a large amount of multivariate …
Deep learning-based anomaly detection and log analysis for computer networks
S Wang, R Jiang, Z Wang, Y Zhou - arxiv preprint arxiv:2407.05639, 2024 - arxiv.org
Computer network anomaly detection and log analysis, as an important topic in the field of
network security, has been a key task to ensure network security and system reliability. First …
network security, has been a key task to ensure network security and system reliability. First …